choose_bvar: Finding the Set of Hyperparameters of Individual Bayesian...

In bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

Finding the Set of Hyperparameters of Individual Bayesian Model

Description

Instead of these functions, you can use choose_bayes().

Usage

choose_bvar(
  bayes_spec = set_bvar(),
  lower = 0.01,
  upper = 10,
  ...,
  eps = 1e-04,
  y,
  p,
  include_mean = TRUE,
  parallel = list()
)

choose_bvhar(
  bayes_spec = set_bvhar(),
  lower = 0.01,
  upper = 10,
  ...,
  eps = 1e-04,
  y,
  har = c(5, 22),
  include_mean = TRUE,
  parallel = list()
)

## S3 method for class 'bvharemp'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

is.bvharemp(x)

## S3 method for class 'bvharemp'
knit_print(x, ...)

Arguments

bayes_spec	Initial Bayes model specification.
lower	Lower bound. By default, .01.
upper	Upper bound. By default, 10.
...	not used
eps	Hyperparameter eps is fixed. By default, 1e-04.
y	Time series data
p	BVAR lag
include_mean	Add constant term (Default: TRUE) or not (FALSE)
parallel	List the same argument of optimParallel::optimParallel(). By default, this is empty, and the function does not execute parallel computation.
har	Numeric vector for weekly and monthly order. By default, c(5, 22).
x	Any object
digits	digit option to print

Details

Empirical Bayes method maximizes marginal likelihood and selects the set of hyperparameters. These functions implement L-BFGS-B method of stats::optim() to find the maximum of marginal likelihood.

If you want to set lower and upper option more carefully, deal with them like as in stats::optim() in order of set_bvar(), set_bvhar(), or set_weight_bvhar()'s argument (except eps). In other words, just arrange them in a vector.

Value

bvharemp class is a list that has

• stats::optim() or optimParallel::optimParallel()

• chosen bvharspec set

• Bayesian model fit result with chosen specification

  ...

  Many components of stats::optim() or optimParallel::optimParallel()

  spec

  Corresponding bvharspec

  fit

  Chosen Bayesian model

  ml

  Marginal likelihood of the final model

References

Byrd, R. H., Lu, P., Nocedal, J., & Zhu, C. (1995). A limited memory algorithm for bound constrained optimization. SIAM Journal on scientific computing, 16(5), 1190-1208.

Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2013). Bayesian data analysis. Chapman and Hall/CRC.

Giannone, D., Lenza, M., & Primiceri, G. E. (2015). Prior Selection for Vector Autoregressions. Review of Economics and Statistics, 97(2).

Kim, Y. G., and Baek, C. (2024). Bayesian vector heterogeneous autoregressive modeling. Journal of Statistical Computation and Simulation, 94(6), 1139-1157.

bvhar documentation built on April 4, 2025, 5:22 a.m.